Perceptron - ~m-cl-nop - logreg - {‘zipmap’: False}#

Fitted on a problem type ~m-cl-nop (see find_suitable_problem), method predict matches output . Model was converted with additional parameter: <class 'sklearn.linear_model._perceptron.Perceptron'>={'zipmap': False}.

Perceptron(n_jobs=8)

index

0

skl_nop

1

skl_ncoef

3

skl_nlin

1

onx_size

730

onx_nnodes

7

onx_ninits

4

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

14

onx_ai.onnx.ml

1

onx_op_Cast

1

onx_op_Identity

1

onx_op_Reshape

1

onx_size_optim

692

onx_nnodes_optim

6

onx_ninits_optim

4

fit_coef_.shape

(3, 4)

fit_intercept_.shape

3

fit_classes_.shape

3
%0 X X float((0, 4)) MatMul MatMul (MatMul) X->MatMul label label int64((0,)) probabilities probabilities float((0, 3)) ArgMax ArgMax (ArgMax) axis=1 probabilities->ArgMax classes classes int32((3,)) [0 1 2] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor coef coef float32((4, 3)) [[  0.76526785   8.838072   -29.584278  ] [  4.03... coef->MatMul intercept intercept float32((3,)) [  1.  15. -16.] Add Add (Add) intercept->Add shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape matmul_result matmul_result matmul_result->Add MatMul->matmul_result score score Identity Identity (Identity) score->Identity Add->score Identity->probabilities predicted_label predicted_label predicted_label->ArrayFeatureExtractor ArgMax->predicted_label final_label final_label final_label->Reshape ArrayFeatureExtractor->final_label reshaped_final_label reshaped_final_label Cast Cast (Cast) to=7 reshaped_final_label->Cast Reshape->reshaped_final_label Cast->label

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Perceptron - ~b-cl-nop - logreg - {‘zipmap’: False}

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Perceptron - ~b-cl-dec - logreg - {‘zipmap’: False}